About

Jin Liu is currently an Associate Professor of Chinese language and culture in the School of Modern Languages at Georgia Tech. She received her Ph.D. in East Asian literature and culture from Cornell University in 2008, along with an M.A. in Chinese linguistics and a B.A. in Chinese language and literature from Beijing University. Her interdisciplinary research studies contemporary Chinese media culture and popular culture from the perspective of language, writing, sound, voice, and music. She is the author of the book, Signifying the Local: Media Productions Rendered in Local Languages in Mainland China in the New Millennium (2013), which examines recent cultural productions in local languages and dialects across film, television, the Internet, popular music, and fiction in mainland China. She also co-edited and contributed to Chinese Under Globalization: Emerging Trends in Language Use in China (2012). Her publications include articles on Chinese independent films, eco-cinema, rap music, Internet culture, youth culture, sociolinguistics, pedagogy, and digital humanities, published in numerous peer-reviewed journals. Dr. Liu is working on her second book exploring the creativity and innovation of Chinese script over 150 years, and she has led interdisciplinary Digital Humanities projects on Chinese literature fractality and computerized tonal congruence in rap songs. She is also building a digital resource center for the cultural study of Chinese dialects. At Georgia Tech, she teaches Chinese language and culture, and has previously taught at Cornell University, Middlebury College, and Princeton University. She has received multiple teaching awards and serves as a board member of the China Research Center, as well as being involved in organizing cultural events and scholarship coordination.

Research topics

• Artificial Intelligence
• Computer Science
• Machine Learning
• Psychiatry
• Psychology
• Neuroscience
• Radiology
• Internal medicine
• Bioinformatics
• Medicine

Selected publications

• A deep learning approach for mental health quality prediction using functional network connectivity and assessment data

  Brain Imaging and Behavior · 2024 · 20 citations

  • Artificial Intelligence
  • Machine Learning
  • Computer Science
  PublisherDOI

• A Deep Learning Approach for Mental Health Quality Prediction Using Functional Network Connectivity and Assessment Data

  bioRxiv (Cold Spring Harbor Laboratory) · 2023 · 2 citations

  • Artificial Intelligence
  • Machine Learning
  • Computer Science

  Abstract While one can characterize mental health using questionnaires, such tools do not provide direct insight into the underlying biology. By linking approaches that visualize brain activity to questionnaires in the context of individualized prediction, we can gain new insights into the biology and behavioral aspects of brain health. Resting-state fMRI (rs-fMRI) can be used to identify biomarkers of these conditions and study patterns of abnormal connectivity. In this work, we estimate mental health quality for individual participants using static functional network connectivity (sFNC) data from rs-fMRI. The deep learning model uses the sFNC data as input to predict four categories of mental health quality and visualize the neural patterns indicative of each group. We used guided gradient class activation maps (guided Grad-CAM) to identify the most discriminative sFNC patterns. The effectiveness of this model was validated using the UK Biobank dataset, in which we showed that our approach outperformed four alternative models by 4-18% accuracy. The proposed model’s performance evaluation yielded a classification accuracy of 76%, 78%, 88%, and 98% for the excellent, good, fair, and poor mental health categories, with poor mental health accuracy being the highest. The findings show distinct sFNC patterns across each group. The patterns associated with excellent mental health consist of the cerebellar-subcortical regions, whereas the most prominent areas in the poor mental health category are in the sensorimotor and visual domains. Thus the combination of rs-fMRI and deep learning opens a promising path for developing a comprehensive framework to evaluate and measure mental health. Moreover, this approach had the potential to guide the development of personalized interventions and enable the monitoring of treatment response. Overall this highlights the crucial role of advanced imaging modalities and deep learning algorithms in advancing our understanding and management of mental health.

  PublisherOA PDFDOI

• Alterations of frontal-temporal gray matter volume associate with clinical measures of older adults with COVID-19

  Neurobiology of Stress · 2021 · 75 citations

  • Medicine
  • Internal medicine
  • Radiology

  COVID-19, the infectious disease caused by the most recently discovered severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has become a global pandemic. It dramatically affects people's health and daily life. Neurological complications are increasingly documented for patients with COVID-19. However, the effect of COVID-19 on the brain is less studied, and existing quantitative neuroimaging analyses of COVID-19 were mainly based on the univariate voxel-based morphometry analysis (VBM) that requires corrections for a large number of tests for statistical significance, multivariate approaches that can reduce the number of tests to be corrected have not been applied to study COVID-19 effect on the brain yet. In this study, we leveraged source-based morphometry (SBM) analysis, a multivariate extension of VBM, to identify changes derived from computed tomography scans in covarying gray matter volume patterns underlying COVID-19 in 120 neurological patients (including 58 cases with COVID-19 and 62 patients without COVID-19 matched for age, gender and diseases). SBM identified that lower gray matter volume (GMV) in superior/medial/middle frontal gyri was significantly associated with a higher level of disability (modified Rankin Scale) at both discharge and six months follow-up phases even when controlling for cerebrovascular diseases. GMV in superior/medial/middle frontal gyri was also significantly reduced in patients receiving oxygen therapy compared to patients not receiving oxygen therapy. Patients with fever presented significant GMV reduction in inferior/middle temporal gyri and fusiform gyrus compared to patients without fever. Patients with agitation showed GMV reduction in superior/medial/middle frontal gyri compared to patients without agitation. Patients with COVID-19 showed no significant GMV differences from patients without COVID-19 in any brain region. Results suggest that COVID-19 may affect the frontal-temporal network in a secondary manner through fever or lack of oxygen.

  DOI

Recent grants

• Genetic Networks Influencing Gray Matter Changes in Persistent ADHD

  NIH · $1.5M · 2016–2021

• NIH Grant R01GM081603

  NIH · $1.5M · 2013

• NIH Grant R21DA027626

  NIH · $412k · 2011

• A decentralized macro and micro gene-by-environment interaction analysis of substance use behavior and its brain biomarkers

  NIH · $2.9M · 2019–2025

• NIH Grant R33DA027626

  NIH · $668k · 2014

Frequent coauthors

• Vince D. Calhoun

  Center for Translational Research in Neuroimaging and Data Science

  1238 shared

• Jiayu Chen

  Chenzhou First People's Hospital

  451 shared

• Zening Fu

  Emory University

  292 shared

• Jessica A. Turner

  The Ohio State University

  253 shared

• Jing Sui

  Georgia State University

  203 shared

• Godfrey D. Pearlson

  Institute for Community Living

  172 shared

• Ingrid Agartz

  University of Oslo

  169 shared

• Stefan Ehrlich

  161 shared

Awards & honors

• Georgia Tech CETL/BP Junior Faculty Teaching Excellence (201…
• Institute-wide award of “Student Recognition of Excellence i…
• Faculty Excellence in Research Award, School of Modern Langu…
• CIOS Honor Roll, Spring 2025
• CIOS Honor Roll, Summer 2024

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